混沌系统的建模与辨识是混沌控制的基础。
Modeling and identifying chaotic system is the basis of chaos control.
用状态观测器方法构造同步混沌接收系统，根据最速下降法辨识未知参数。
A synchronous chaotic receive system was constructed by a state observer method, and the unknown parameters were identified by steepest descend method.
针对不确定非线性混沌系统，提出了一种基于动态神经网络辨识器的自适应跟踪控制新方法。
An adaptive tracking controller based on dynamical neural network identifier for uncertain nonlinear chaos systems is presented.
在切换流形控制混沌系统同步的基础上，提出一种基于系统辨识的自适应混沌同步控制策略。
Based on the switching manifold approach to chaos synchronization, a controlling strategy of adaptive chaotic synchronization based on system identification is presented.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
本文研究了电子和电路系统存在的混沌现象的神经网络控制问题，其中包括两个方面的内容：混沌系统的神经网络辨识技术和基于神经网络的混沌控制。
In this paper, research concentrates on the neural network control of chaotic systems in electronics and circuits systems, including two fields: chaotic systems identification and chaotic control.
针对一类时延混沌系统参数未知的情况，将自适应技术与系统辨识技术应用于时延混沌系统的同步控制。
The synchronization of a class of time-delay chaotic systems with unknown parameters is investigated and an adaptive control strategy is proposed.
针对一类时延混沌系统参数未知的情况，将自适应技术与系统辨识技术应用于时延混沌系统的同步控制。
The synchronization of a class of time-delay chaotic systems with unknown parameters is investigated and an adaptive control strategy is proposed.
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